11,504 research outputs found
Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks
The gene regulatory network (GRN) is the central decisionâmaking module of the cell. We have developed a theory called Buffered Qualitative Stability (BQS) based on the hypothesis that GRNs are organised so that they remain robust in the face of unpredictable environmental and evolutionary changes. BQS makes strong and diverse predictions about the network features that allow stable responses under arbitrary perturbations, including the random addition of new connections. We show that the GRNs of E. coli, M. tuberculosis, P. aeruginosa, yeast, mouse, and human all verify the predictions of BQS. BQS explains many of the small- and largeâscale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response. BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation. DOI: http://dx.doi.org/10.7554/eLife.02863.00
Crosstalk and the Dynamical Modularity of Feed-Forward Loops in Transcriptional Regulatory Networks
Network motifs, such as the feed-forward loop (FFL), introduce a range of complex behaviors to transcriptional regulatory networks, yet such properties are typically determined from their isolated study. We characterize the effects of crosstalk on FFL dynamics by modeling the cross regulation between two different FFLs and evaluate the extent to which these patterns occur in vivo. Analytical modeling suggests that crosstalk should overwhelmingly affect individual protein-expression dynamics. Counter to this expectation we find that entire FFLs are more likely than expected to resist the effects of crosstalk (approximate to 20% for one crosstalk interaction) and remain dynamically modular. The likelihood that cross-linked FFLs are dynamically correlated increases monotonically with additional crosstalk, but is independent of the specific regulation type or connectivity of the interactions. Just one additional regulatory interaction is sufficient to drive the FFL dynamics to a statistically different state. Despite the potential for modularity between sparsely connected network motifs, Escherichia coli (E. coli) appears to favor crosstalk wherein at least one of the cross-linked FFLs remains modular. A gene ontology analysis reveals that stress response processes are significantly overrepresented in the cross-linked motifs found within E. coli. Although the daunting complexity of biological networks affects the dynamical properties of individual network motifs, some resist and remain modular, seemingly insulated from extrinsic perturbations-an intriguing possibility for nature to consistently and reliably provide certain network functionalities wherever the need arise
Information content based model for the topological properties of the gene regulatory network of Escherichia coli
Gene regulatory networks (GRN) are being studied with increasingly precise
quantitative tools and can provide a testing ground for ideas regarding the
emergence and evolution of complex biological networks. We analyze the global
statistical properties of the transcriptional regulatory network of the
prokaryote Escherichia coli, identifying each operon with a node of the
network. We propose a null model for this network using the content-based
approach applied earlier to the eukaryote Saccharomyces cerevisiae. (Balcan et
al., 2007) Random sequences that represent promoter regions and binding
sequences are associated with the nodes. The length distributions of these
sequences are extracted from the relevant databases. The network is constructed
by testing for the occurrence of binding sequences within the promoter regions.
The ensemble of emergent networks yields an exponentially decaying in-degree
distribution and a putative power law dependence for the out-degree
distribution with a flat tail, in agreement with the data. The clustering
coefficient, degree-degree correlation, rich club coefficient and k-core
visualization all agree qualitatively with the empirical network to an extent
not yet achieved by any other computational model, to our knowledge. The
significant statistical differences can point the way to further research into
non-adaptive and adaptive processes in the evolution of the E. coli GRN.Comment: 58 pages, 3 tables, 22 figures. In press, Journal of Theoretical
Biology (2009)
Mathematical modelling plant signalling networks
During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This sub-cellular analysis paves the way for more comprehensive mathematical studies of hormonal transport and signalling in a multi-scale setting
Principles of microRNA regulation of a human cellular signaling network
MicroRNAs (miRNAs) are endogenous 22-nucleotide RNAs, which suppress gene
expression by selectively binding to the 3-noncoding region of specific message
RNAs through base-pairing. Given the diversity and abundance of miRNA targets,
miRNAs appear to functionally interact with various components of many cellular
networks. By analyzing the interactions between miRNAs and a human cellular
signaling network, we found that miRNAs predominantly target positive
regulatory motifs, highly connected scaffolds and most downstream network
components such as signaling transcription factors, but less frequently target
negative regulatory motifs, common components of basic cellular machines and
most upstream network components such as ligands. In addition, when an adaptor
has potential to recruit more downstream components, these components are more
frequently targeted by miRNAs. This work uncovers the principles of miRNA
regulation of signal transduction networks and implies a potential function of
miRNAs for facilitating robust transitions of cellular response to
extracellular signals and maintaining cellular homeostasis
Large-scale inference and graph theoretical analysis of gene-regulatory networks in B. stubtilis
We present the methods and results of a two-stage modeling process that
generates candidate gene-regulatory networks of the bacterium B. subtilis from
experimentally obtained, yet mathematically underdetermined microchip array
data. By employing a computational, linear correlative procedure to generate
these networks, and by analyzing the networks from a graph theoretical
perspective, we are able to verify the biological viability of our inferred
networks, and we demonstrate that our networks' graph theoretical properties
are remarkably similar to those of other biological systems. In addition, by
comparing our inferred networks to those of a previous, noisier implementation
of the linear inference process [17], we are able to identify trends in graph
theoretical behavior that occur both in our networks as well as in their
perturbed counterparts. These commonalities in behavior at multiple levels of
complexity allow us to ascertain the level of complexity to which our process
is robust to noise.Comment: 22 pages, 4 figures, accepted for publication in Physica A (2006
An evolutionary and functional assessment of regulatory network motifs.
BackgroundCellular functions are regulated by complex webs of interactions that might be schematically represented as networks. Two major examples are transcriptional regulatory networks, describing the interactions among transcription factors and their targets, and protein-protein interaction networks. Some patterns, dubbed motifs, have been found to be statistically over-represented when biological networks are compared to randomized versions thereof. Their function in vitro has been analyzed both experimentally and theoretically, but their functional role in vivo, that is, within the full network, and the resulting evolutionary pressures remain largely to be examined.ResultsWe investigated an integrated network of the yeast Saccharomyces cerevisiae comprising transcriptional and protein-protein interaction data. A comparative analysis was performed with respect to Candida glabrata, Kluyveromyces lactis, Debaryomyces hansenii and Yarrowia lipolytica, which belong to the same class of hemiascomycetes as S. cerevisiae but span a broad evolutionary range. Phylogenetic profiles of genes within different forms of the motifs show that they are not subject to any particular evolutionary pressure to preserve the corresponding interaction patterns. The functional role in vivo of the motifs was examined for those instances where enough biological information is available. In each case, the regulatory processes for the biological function under consideration were found to hinge on post-transcriptional regulatory mechanisms, rather than on the transcriptional regulation by network motifs.ConclusionThe overabundance of the network motifs does not have any immediate functional or evolutionary counterpart. A likely reason is that motifs within the networks are not isolated, that is, they strongly aggregate and have important edge and/or node sharing with the rest of the network
Feedbacks from the metabolic network to the genetic network reveal regulatory modules in E. coli and B. subtilis
The genetic regulatory network (GRN) plays a key role in controlling the
response of the cell to changes in the environment. Although the structure of
GRNs has been the subject of many studies, their large scale structure in the
light of feedbacks from the metabolic network (MN) has received relatively
little attention. Here we study the causal structure of the GRNs, namely the
chain of influence of one component on the other, taking into account feedback
from the MN. First we consider the GRNs of E. coli and B. subtilis without
feedback from MN and illustrate their causal structure. Next we augment the
GRNs with feedback from their respective MNs by including (a) links from genes
coding for enzymes to metabolites produced or consumed in reactions catalyzed
by those enzymes and (b) links from metabolites to genes coding for
transcription factors whose transcriptional activity the metabolites alter by
binding to them. We find that the inclusion of feedback from MN into GRN
significantly affects its causal structure, in particular the number of levels
and relative positions of nodes in the hierarchy, and the number and size of
the strongly connected components (SCCs). We then study the functional
significance of the SCCs. For this we identify condition specific feedbacks
from the MN into the GRN by retaining only those enzymes that are essential for
growth in specific environmental conditions simulated via the technique of flux
balance analysis (FBA). We find that the SCCs of the GRN augmented by these
feedbacks can be ascribed specific functional roles in the organism. Our
algorithmic approach thus reveals relatively autonomous subsystems with
specific functionality, or regulatory modules in the organism. This automated
approach could be useful in identifying biologically relevant modules in other
organisms for which network data is available, but whose biology is less well
studied.Comment: 15 figure
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